#!/usr/bin/env python3
"""
CogVideo Inference Server using FastAPI.
"""

import os
import logging
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import FileResponse
import uvicorn

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# --- FastAPI App ---
app = FastAPI(title="CogVideo Server")

# --- Model Loading ---
# Placeholder for model loading logic
# This should be done once when the server starts.
logger.info("Loading CogVideo model...")
# model = load_cogvideo_model()
logger.info("✓ CogVideo model loaded.")

# --- API Endpoints ---
@app.get("/health")
def health_check():
    """Health check endpoint."""
    return {"status": "ok"}

@app.post("/generate")sync def generate_video(image: UploadFile = File(...)):
    """Generate a video from an input image."""
    # 1. Save the uploaded image
    upload_path = os.path.join("uploads", image.filename)
    with open(upload_path, "wb") as buffer:
        buffer.write(await image.read())

    # 2. Run CogVideo inference
    # output_path = run_inference(model, upload_path)
    output_path = "outputs/generated.mp4" # Placeholder

    # 3. Return the generated video
    return FileResponse(output_path, media_type="video/mp4", filename="generated.mp4")

# --- Main Execution ---
if __name__ == "__main__":
    uvicorn.run(
        app,
        host="0.0.0.0",
        port=8080,
    )

